Nextdoor·Machine Learning Engineer·Take-home Assignment·Senior

Senior

Jun 2026

Summary

Nextdoor ML engineer take-home where you build a full classification pipeline in Colab on a tabular dataset they provide. Pretty standard end-to-end exercise but they actually want you to talk through your decisions live, not just hand in a notebook with good numbers.

Questions Asked (1)

Build an end-to-end classification model on a provided tabular dataset in a shared notebook environment. Cover data exploration, preprocessing, modeling, evaluation, and iteration while explaining your decisions out loud.

Technical Trade-offsData ModelingProduct Analytics & Metrics

Author's notes

The part that tripped me up was the 'communicate as you go' expectation.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem and clarifying success metrics with the interviewer, then walk through a structured pipeline: EDA, preprocessing, baseline model, evaluation, and iteration. Narrate your reasoning at each step, emphasizing trade-offs and how you would validate decisions with data.

Pro tip: Before diving into modeling, establish a simple baseline (e.g., logistic regression or majority class) and a clear evaluation metric aligned with business impact—this shows you prioritize measurable progress over complexity. Also, explicitly state assumptions and ask clarifying questions to demonstrate collaboration and product sense.

1. Understand the Problem and Data

Clarify the prediction target, business objective, and success metrics. Perform initial EDA: check data types, missing values, distributions, and class balance.

2. Preprocess and Feature Engineer

Handle missing values, encode categorical variables, scale numerical features, and create relevant features. Explain each choice and its impact on model performance.

3. Build and Evaluate Baseline Models

Start with a simple baseline (e.g., logistic regression) and evaluate using appropriate metrics (e.g., AUC-ROC, F1). Compare against a naive baseline to gauge lift.

4. Iterate with Advanced Models and Tuning

Try more complex models (e.g., tree-based ensembles) and hyperparameter tuning. Use cross-validation and track experiments to avoid overfitting.

5. Interpret, Communicate, and Plan Next Steps

Analyze feature importance, error patterns, and business implications. Summarize findings and propose improvements or deployment considerations.

Key Points to Mention

Clarify evaluation metrics early (e.g., precision/recall trade-off, AUC) based on business costs.
Handle class imbalance appropriately (e.g., resampling, class weights) and discuss its impact.
Use cross-validation and avoid data leakage during preprocessing.
Start simple: baseline models provide a benchmark and reveal data issues.
Explain model choices (e.g., why XGBoost over logistic regression) in terms of bias-variance and interpretability.
Communicate results to non-technical stakeholders and suggest next steps (e.g., feature collection, A/B testing).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.